¹ Nadeau, Richard, Niemi, Richard and Amato, Timothy, ‘Prospective and Comparative or Retrospective and Individual? Party Leaders and Party Support in Great Britain’, British Journal of Political Science, 26 (1996), 245–258CrossRefGoogle Scholar; Clarke, Harold, Mishler, William and Whiteley, Paul, ‘Recapturing the Falklands: Models of Conservative Popularity, 1979–1983’, British Journal of Political Science, 20 (1990), 63–81CrossRefGoogle Scholar; and Crespi, Irving, ‘The Case of Presidential Popularity’, in A. Cantril, eds, Polling on the Issues (Washington, D.C.: Seven Locks Press, 1980)Google Scholar.

² Bingham Powell, G. and Whitten, Guy, ‘A Cross-National Analysis of Economic Voting: Taking Account of the Political Context’, American Journal of Political Science, 37 (1993), 391–414CrossRefGoogle Scholar; and Persson, Torsten and Tabellini, Guido, Political Economics: Explaining Economic Policy (Cambridge, Mass.: MIT Press, 2000)Google Scholar.

³ For example, compare vote choice studies, such as Fiorina, Morris P., ‘Economic Retrospective Voting in American National Elections: A Micro-Analysis’, American Journal of Political Science, 22 (1978), 426–443CrossRefGoogle Scholar; Fiorina, Morris P., Retrospective Voting in American National Elections (New Haven, Conn.: Yale University Press, 1981)Google Scholar; and Kiewiet, D. R., Macroeconomics and Micropolitics: The Electoral Effects of Economic Issues (Chicago: University of Chicago Press, 1983)Google Scholar, to presidential approval studies, such as MacKuen, Michael B., Erikson, Robert S. and Stimson, James A., ‘Peasants or Bankers? The American Electorate and the US Economy’, American Political Science Review, 86 (1992), 597–611CrossRefGoogle Scholar.

⁴ Paldam, Martin, ‘How Robust Is the Vote Function? A Study of 17 Nations over Four Decades’, in Helmut Norpoth, Michael S. Lewis-Beck and Jean-Dominique LaFay, eds, Economics and Politics: The Calculus of Support (Ann Arbor: The University of Michigan Press, 1991).Google Scholar

⁵ Key, V. O. Jr, The Responsible Electorate: Rationality in Presidential Voting: 1936–1960 (Cambridge, Mass.: The Belknap Press of Harvard University Press, 1966)CrossRefGoogle Scholar; Key, V. O. Jr, Politics, Parties, and Pressure Groups, 5th edn (New York: Thomas Y. Crowell, 1964)Google Scholar; and Downs, Anthony, An Economic Theory of Democracy (New York: Harper and Row, 1957)Google Scholar. Important works that are motivated in this way include: Norpoth, Helmut, ‘Presidents and the Prospective Voter’, Journal of Politics, 58 (1996), 776–792; MacKuen, Erikson and Stimson, ‘Peasants or Bankers?’CrossRefGoogle Scholar; Chappell, Henry W. and Keech, William R., ‘A New View of Political Accountability for Economic Performance’, American Political Science Review, 79 (1985), 10–27CrossRefGoogle Scholar; and Beck, Nathaniel, ‘The Economy and Presidential Approval: An Information Theoretical Perspective’, in Norpoth, Lewis-Beck and LaFay, eds, Economics and PoliticsGoogle Scholar. There are also, of course, well-known examples of US economic popularity studies that are explicitly concerned with presidential approval and are motivated as such. For example: Hibbs, Douglas A. Jr, ‘On the Domain for Economic Outcomes: Macroeconomic Performance and Mass Political Support in the United States, Great Britain, and Germany’, Journal of Politics, 44 (1982), 426–462CrossRefGoogle Scholar; and Kernell, Samuel, ‘Explaining Presidential Popularity’, American Political Science Review, 72 (1978), 506–522CrossRefGoogle Scholar.

⁶ For example, Key, , The Responsible Electorate, pp. 58–62.Google Scholar

⁷ For example, Norpoth, Helmut, ‘Presidents and the Prospective Voter’, and Morris Fiorina, Retrospective Voting in American National Elections New Haven, Conn.: Yale University Press, 1981).Google Scholar

⁸ For example, Frey, Bruno and Schneider, Friedrich, ‘An Empirical Study of Political-Economic Interaction in the United States’, Review of Economics and Statistics, 60 (1978), 174–183; MacKuen, Erikson and Stimson, ‘Peasants or Bankers?’CrossRefGoogle Scholar; and Mueller, John E., ‘Presidential Popularity from Truman to Johnson’, American Political Science Review, 64 (1970), 18–34CrossRefGoogle Scholar.

⁹ Beck, , ‘The Economy and Presidential Approval’.Google Scholar

¹⁰ On the dampening of vote intention volatility produced by partisan identification see: Converse, Philip E. and Dupeux, Georges, ‘Politicization of the Electorate in France and the United States’, Public Opinion Quarterly, 26 (1962), 1–23CrossRefGoogle Scholar; and Kayser, Mark and Wlezien, Christopher, ‘Performance Pressure: Patterns of Partisanship and the Economic Vote’ (unpublished manuscript, 2007)Google Scholar.

¹¹ On vote choice: Fiorina, ‘Economic Retrospective Voting in American National Elections’; Fiorina, Retrospective Voting in American National Elections; and Kiewiet, Macroeconomics and Micropolitics. On presidential approval: MacKuen, Erikson and Stimson, ‘Peasants or Bankers?’

¹² Erikson, Robert S. and Wlezien, Christopher, ‘Presidential Polls as a Timeseries: The Case of 1996’, Public Opinion Quarterly, 63 (1999), 163–177CrossRefGoogle Scholar; Jackman, Simon, ‘Pooling the Polls over an Election Campaign’, Australian Journal of Political Science, 40 (2005), 499–517CrossRefGoogle Scholar; Pickup, Mark and Johnston, Richard, ‘Campaign Trial Heats as Election Forecasts: Measurement Error and Bias in 2004 Presidential Campaign Polls’, International Journal of Forecasting, 24 (2008), 272–284CrossRefGoogle Scholar; and Pickup, Mark and Johnston, Richard, ‘Campaign Trial Heats as Election Forecasts: Evidence from the 2004 and 2006 Canadian Elections’, Electoral Studies, 26 (2007), 460–476CrossRefGoogle Scholar.

¹³ Campbell, James E., ‘Forecasting the Presidential Vote in 2004: Placing Preference Polls in Context’, PS: Political Science & Politics, 37 (2004), 763–767Google Scholar; Campbell, James E., ‘Evaluating the Trial-Heat and Economy Forecast of the 2004 Presidential Vote: All’s Well that Ends Well’, PS: Political Science & Politics, 38 (2006), 763–767Google Scholar; Whiteley, Paul, ‘Electoral Forecasting from Poll Data: The British Case’, British Journal of Political Science, 9 (1979), 219–236CrossRefGoogle Scholar; and Kayser and Wlezien, ‘Performance Pressure: Patterns of Partisanship and the Economic Vote’.

¹⁴ Erikson, Robert S., Bafumi, Joseph and Wilson, Bret, ‘Could the Close 2000 Elections Have Been Predicted?’, PS: Political Science and Politics, 34 (2001), 815–819.Google Scholar

¹⁵ Wlezien, Christopher and Erikson, Robert S., ‘Temporal Horizons and Presidential Election Forecasts’, American Politics Quarterly, 24 (1996), 492–505.CrossRefGoogle Scholar

¹⁶ Christopher Wlezien graciously provided the necessary data.

¹⁷ Samuel, Kernell and Douglas, Hibbs Jr, ‘A Critical Threshold Model of Presidential Popularity’, in D. A. Hibbs, Jr and H. Fassbender, eds, Contemporary Political Economy (Amsterdam: North-Holland, 1981).Google Scholar

¹⁸ Campbell, Angus, Converse, Philip, Miller, Warren and Stokes, Donald, The American Voter (New York: Wiley, 1960)Google Scholar; Miller, Warren E. and Stokes, Donald E., ‘Constituency Influence in Congress’, American Political Science Review, 57 (1963), 45–56CrossRefGoogle Scholar; Butler, David and Stokes, Donald, Political Change in Britain: The Evolution of Electoral Choice (London: Macmillan, 1974)CrossRefGoogle Scholar; and Budge, Ian, Crewe, Ivor and Farlie, Dennis, Party Identification and beyond: Representations of Voting in the Party Competition (London: John Wiley, 1976)Google Scholar.

¹⁹ The two inequalities simply require that a government identifier is not going to vote for an opposition party/candidate while still approving of the government and an opposition identifier is not going to vote for the government while they disapprove of it.

²⁰ See Appendix, Note 1, in the Supplementary Materials on the website 〈journals.cambridge.org/jps〉 for a demonstration of why this is always true.

²¹ Butler, David and Stokes, Donald, Political Change in Britain (New York: St Martin’s, 1969); and Budge, Crewe and Farlie, Party Identification and Beyond.Google Scholar

²² Brynin, Malcolm and Sanders, David, ‘Party Identification, Political Preferences and Material Conditions’, Party Politics, 3 (1997), 53–77.CrossRefGoogle Scholar

²³ Lanoue, D. J. and Bowler, S., ‘The Sources of Tactical Voting in British Parliamentary Elections, 1983–1987’, Political Behaviour, 14 (1992), 141–157.CrossRefGoogle Scholar

²⁴ Cain, Bruce E., ‘Strategic Voting in Britain’, American Journal of Political Science, 22 (1978), 639–655CrossRefGoogle Scholar; Evans, Geoffrey, ‘Tactical Voting and Labour's Prospects’, in Anthony Heath, Roger Jowell and John Curtis, eds, Labour's Last Chance? The 1992 Election and Beyond (Aldershot, Hants.: Dartmouth, 1994)Google Scholar; and Fisher, Stephen and Curtis, John, ‘Tactical Unwind? Changes in Party Preference Structure and Tactical Voting in Britain between 2001 in 2005’, Journal of Elections, Public Opinion and Parties, 16 (2006), 55–76CrossRefGoogle Scholar.

²⁵ Plots of the data and the rationale for the time period studied for each case is provided in the Appendix, Note 2, in the Supplementary Materials on the website 〈journals.cambridge.org/jps〉.

²⁶ Note 8, in the Supplementary Materials on the website 〈journals.cambridge.org/jps〉.

²⁷ The mean squared perturbation is similar to the variance but squares the difference between a month’s value and the previous month’s rather than the series’ mean. This is more appropriate in this context, as it is the month-to-month stability that is of greatest interest and the variance will overstate the month-to-month instability in a series that trends.

²⁸ Further details are provided in the Appendix, Note 3, in the Supplementary Materials on the website 〈journals.cambridge.org/jps〉.

²⁹ FPE = final prediction error, AIC = Akaike’s information criterion, SBIC = Schwarz’s Bayesian information criterion and HQIC = Hannan and Quinn information criterion.

³⁰ This produces a structural VAR that has a recursive structure and so a causal interpretation can be applied.

³¹ For a description of the economic data used in these models, see Appendix, Note 4, in the Supplementary Materials on the website 〈journals.cambridge.org/jps〉.

³² The post-election trending is modelled using three count variables that begin the month after each election.

³³ Clarke, Harold and Lebo, Matthew, ‘Fractional (Co)integration and Governing Party Support in Britain’, British Journal of Political Science, 33 (2003), 283–301.CrossRefGoogle Scholar

³⁴ The impact of 9/11 is estimated using a dummy that equals one for the month of the event and the four months following. The impact of the second Iraq war is estimated by a variable that equals the cumulative number of British casualties in Iraq.

³⁵ The Appendix, Note 5, in the Supplementary Materials on the website 〈journals.cambridge.org/jps〉, describes the transformation for this particular ECM. For further details, see Banerjee, Anindya, Dolado, Juan, Galbraith, John W. and Hendry, David F., Co-integration, Error-Correction, and the Econometric Analysis of Non-Stationary Data (Oxford: Oxford University Press, 1993)CrossRefGoogle Scholar; and Hendry, David F., Dynamic Econometrics (Oxford: Oxford University Press, 1995)CrossRefGoogle Scholar. For an example of the use of the dead-start autoregressive distributive-lag model in the British context, see David Sanders, ‘The Real Economy and the Perceived Economy in Popularity Functions: How Much Do Voters Need to Know? A Study of British Data, 1974–97’, Electoral Studies, 19 (2000), 275–294Google Scholar.

³⁶ The ECMs were estimated in WinBUGS. See the Appendix, Note 6, in the Supplementary Materials on the website 〈journals.cambridge.org/jps〉 for further estimation details.

³⁷ The direction of the effect is as we would expect for unemployment but a little peculiar for inflation.

³⁸ Note that the largest one-month increase during this period is 0.2 percentage points.

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⁴¹ The Appendix, Note 3, in the Supplementary Materials on the website 〈journals.cambridge.org/jps〉, provides a translation of these questions.

⁴² These complications include the fact that the New Federal States (former East Germany) are substantively different, both economically and politically, than the Old Federal States (Feld, Lars P. and Kirchgässner, Gebhard, ‘Official and Hidden Unemployment in the Popularity of the Government: An Econometric Analysis for the Kohl Government’, Electoral Studies, 19 (2000), 333–347.CrossRefGoogle Scholar

⁴³ See the Appendix, Note 2, in the Supplementary Materials on the website 〈journals.cambridge.org/jps〉, for the relevant figure.

⁴⁴ The cycling is captured by including two terms in the regression: [Θ₁(sin (λθ)] and [Θ₂(cos (λθ)], where Θ₁ and Θ₂ are the parameters to be estimated and λ is the frequency (1/wavelength) of the popularity cycle, which is defined by the length of the inter-election period. Estimated parameters Θ₁ and Θ₂ can be used to calculate the phase for the inter-election cycle.

⁴⁵ The correlation is also relatively large at the individual level, ranging in any given month from 0.6 to 0.7.

⁴⁶ As before, the assumption of covariance stationarity requires that non-stationary dynamics be accounted for in the models. As it turns out though, once economic variables are included in the following models, the inter-election cycle is statistically insignificant. Therefore, for the sake of efficiency, the ECMs only include the trending term.

⁴⁷ For a description of the economic data used in these models, see the Appendix, Note 4, in the Supplementary Materials on the website 〈journals.cambridge.org/jps〉.

⁴⁸ Campbell, Converse, Miller and Stokes, The American Voter.

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⁵² For arguments against the decline, see: Green, Donald and Palmquist, Bradley, ‘How Stable is Party Identification’, Political Behaviour, 43 (1994), 437–466CrossRefGoogle Scholar; and Green, Donald and Palmquist, Bradley, ‘Of Artefacts and Partisan Instability’, American Journal of Political Science, 34 (1990), 872–902CrossRefGoogle Scholar. For the arguments against the resurgence, see: Fiorina, Morris, ‘Parties and Partisanship: A 40-Year Retrospective’, Political Behaviour, 24 (2002), 93–115CrossRefGoogle Scholar.

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⁶⁰ No constant is included in the model, so no month needs to be excluded as a reference.

⁶¹ As this is a fairly standard technique discussed at length elsewhere, the details of the estimation are relegated to the Appendix, Note 7, in the Supplementary Materials on the website 〈journals.cambridge.org/jps〉. For a more lengthy discussion on this technique, see Pickup and Johnston, ‘Campaign Trial Heats as Election Forecasts’.

⁶² For a description of the economic data, see the Appendix, Note 4, in the Supplementary Materials on the website 〈journals.cambridge.org/jps〉.

⁶³ France: Lafay, J.-D., ‘Political Change and Stability of the Popularity Function: The French General Election of 1981’, in H. Eulau and M.S. Lewis-Beck, eds, Economic Conditions and Electoral Outcomes: The United States and Western Europe (New York: Agathon, 1985), pp.78–97Google Scholar; and Lafay, J.-D., ‘Political Dyarchy and Popularity Functions: Lessons from the 1986 French Experience’, in Norpoth, Lewis-Beck, and Lafay, eds, Economics and Politics, pp. 123–139Google Scholar. Latin America: Weyland, Kurt, ‘Peasants or Bankers in Venezuela? Presidential Popularity and Economic Reform Approval, 1989–1993’, Political Research Quarterly, 51 (1998), 341–362CrossRefGoogle Scholar; and Davis, Charles L. and Langley, Ronald E., ‘Presidential Popularity in a Context of Economic Crisis and Political Change: The Case of Mexico’, Studies in Comparative International Development, 30 (1995), 24–48CrossRefGoogle Scholar.

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⁶⁶ Such models use actual electoral outcomes as the dependent variable.